The current document is directed to methods and systems that employ call traces collected by one or more call-trace services to generate call-trace-classification rules to facilitate root-cause analysis of distributed-application operational problems and failures. In a described implementation, a set of automatically labeled call traces is partitioned by the generated call-trace-classification rules. Call-trace-classification-rule generation is constrained to produce relatively simple rules with greater-than-threshold confidences and coverages. The call-trace-classification rules may point to particular services and service failures, which provides useful information to distributed-application and distributed-computer-system managers and administrators attempting to diagnose operational problems and failures that arise during execution of distributed applications within distributed computer systems. A first dataset is collected during normal distributed-application operation and a second dataset is collected during problem-associated or failure-associated operation of the distributed application. The first and second datasets are used to generate noise-subtracted call-trace-classification rules and/or diagnostic suggestions.
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5. The system of claim 3 wherein a new call-trace-classification rule is pruned by removing terminal conditions from the new call-trace-classification rule until a metric value associated with the new call-trace-classification rule is maximized.
6. The system of claim 3 wherein the system filters the call-trace-classification-rule set by removing those call-trace-classification rules with coverages less than a threshold coverage and/or with confidences less than a threshold confidence.
7. The system of claim 6 wherein the coverage of a call-trace-classification rule is determined as the ratio of a number of call traces selected by the call-trace-classification rule from a labeled call-trace dataset that contain a possible label value corresponding to the label in the set of labels to a number of call traces in the labeled call-trace dataset that contain the possible label value corresponding to the label in the set of labels.
8. The system of claim 6 wherein the confidence of a call-trace-classification rule is determined as the ratio of a number of call traces selected by the call-trace-classification rule from a labeled call-trace dataset that contain a possible label value corresponding to the label in the set of labels to a number of call traces in the labeled call-trace dataset selected by the call-trace-classification rule.
9. The system of claim 1 wherein a noise-subtracted call-trace-classification rule is generated by one or more set-difference operations on call-trace-classification rules generated from a non-normal-operation dataset that includes call traces collected during one or more time intervals of non-normal operation of the distributed application.
10. The system of claim 9 wherein a set-difference operation returns call-trace-classification rules generated from a non-normal-operation dataset that are not generated from a normal-operation dataset that includes call traces collected during one or more time intervals of normal operation of the distributed application.
14. The system of claim 12 wherein a noise-subtracted diagnostic suggestion is generated by one or more set-difference operations on diagnostic suggestions generated from a non-normal-operation dataset that includes call traces collected during one or more time intervals of non-normal operation of the distributed application.
15. The system of claim 14 wherein a set-difference operation returns diagnostic suggestions generated from a non-normal-operation dataset that are not generated from a normal-operation dataset that includes call traces collected during one or more time intervals of normal operation of the distributed application.
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October 1, 2021
January 23, 2024
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